×

Instrumental variable estimation of early treatment effect in randomized screening trials. (English) Zbl 07469148

Summary: The primary analysis of randomized screening trials for cancer typically adheres to the intention-to-screen principle, measuring cancer-specific mortality reductions between screening and control arms. These mortality reductions result from a combination of the screening regimen, screening technology and the effect of the early, screening-induced, treatment. This motivates addressing these different aspects separately. Here we are interested in the causal effect of early versus delayed treatments on cancer mortality among the screening-detectable subgroup, which under certain assumptions is estimable from conventional randomized screening trial using instrumental variable type methods. To define the causal effect of interest, we formulate a simplified structural multi-state model for screening trials, based on a hypothetical intervention trial where screening detected individuals would be randomized into early versus delayed treatments. The cancer-specific mortality reductions after screening detection are quantified by a cause-specific hazard ratio. For this, we propose two estimators, based on an estimating equation and a likelihood expression. The methods extend existing instrumental variable methods for time-to-event and competing risks outcomes to time-dependent intermediate variables. Using the multi-state model as the basis of a data generating mechanism, we investigate the performance of the new estimators through simulation studies. In addition, we illustrate the proposed method in the context of CT screening for lung cancer using the US National Lung Screening Trial data.

MSC:

62Nxx Survival analysis and censored data
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

R; Muhaz; mstate; cmprsk
PDF BibTeX XML Cite
Full Text: DOI arXiv

References:

[1] Aalen, OO; Cook, RJ; Røysland, K., Does Cox analysis of a randomized survival study yield a causal treatment effect?, Lifetime Data Anal, 21, 579-593 (2015) · Zbl 1333.62228
[2] Altstein, LL; Li, G., Latent subgroup analysis of a randomized clinical trial through a semiparametric accelerated failure time mixture model, Biometrics, 69, 52-61 (2013) · Zbl 1271.62248
[3] Altstein, LL; Li, G.; Elashoff, RM, A method to estimate treatment efficacy among latent subgroups of a randomized clinical trial, Stat Med, 30, 709-717 (2011)
[4] Angrist, JD; Imbens, GW; Rubin, DB, Identification of causal effects using instrumental variables, J Am Stat Assoc, 91, 444-455 (1996) · Zbl 0897.62130
[5] Baiocchi, M.; Cheng, J.; Small, DS, Instrumental variable methods for causal inference, Stat Med, 33, 2297-2340 (2014)
[6] Baker, SG, Analysis of survival data from a randomized trial with all-or-none compliance: estimating the cost-effectiveness of a cancer screening program, J Am Stat Assoc, 93, 929-934 (1998)
[7] Baker, SG; Lindeman, KS, The paired availability design: a proposal for evaluating epidural analgesia during labor, Stat Med, 13, 21, 2269-2278 (1994)
[8] Baker, SG; Kramer, BS; Lindeman, KS, Latent class instrumental variables: a clinical and biostatistical perspective, Stat Med, 35, 1, 147-160 (2016)
[9] Beyersmann J, Allignol A, Schumacher M (2011) Competing risks and multistate models with R. Springer Science & Business Media · Zbl 1304.62002
[10] Borgan, Ø., Aalen-Johansen estimator, 1-13 (2014), Wiley StatsRef: Statistics Reference Online, Wiley StatsRef
[11] Burgess, S.; Small, DS; Thompson, SG, A review of instrumental variable estimators for mendelian randomization, Stat Methods Med Res, 26, 2333-2355 (2017)
[12] Cube von M, Schumacher M, Wolkewitz M (2019) Causal inference with multistate models-estimands and estimators of the population attributable fraction. J R Stat Soc: Series A (Statistics in Society)
[13] de Wreede LC, Fiocco M, Putter H (2011) mstate: An R package for the analysis of competing risks and multi-state models. J Stat Softw 38:1-30, http://www.jstatsoft.org/v38/i07/
[14] Gray B (2019) cmprsk: Subdistribution Analysis of Competing Risks. https://CRAN.R-project.org/package=cmprsk, r package version 2.2-9
[15] Habbema, D., Statistical analysis and decision making in cancer screening, Eur J Epidemiol, 33, 433-435 (2018)
[16] Hanley, JA; Njor, SH, Disaggregating the mortality reductions due to cancer screening: model-based estimates from population-based data, Eur J Epidemiol, 33, 465-472 (2018)
[17] Hess K, Gentleman R (2019) muhaz: Hazard Function Estimation in Survival Analysis. https://CRAN.R-project.org/package=muhaz, r package version 1.2.6.1
[18] Howe, CJ; Cole, SR; Lau, B.; Napravnik, S.; Eron, JJ Jr, Selection bias due to loss to follow up in cohort studies, Epidemiology (Cambridge, Mass), 27, 91 (2016)
[19] Hu, P.; Zelen, M., Planning clinical trials to evaluate early detection programmes, Biometrika, 84, 817-829 (1997) · Zbl 1090.62572
[20] Imbens, GW; Angrist, JD, Identification and estimation of local average treatment effects, Econometrica, 62, 467-475 (1994) · Zbl 0800.90648
[21] Lee S, Zelen M (2006) Chapter 11: a stochastic model for predicting the mortality of breast cancer. JNCI Monographs 2006:79-86
[22] Liu, Z.; Hanley, JA; Strumpf, EC, Projecting the yearly mortality reductions due to a cancer screening programme, J Med Screen, 20, 157-164 (2013)
[23] Liu, Z.; Hanley, JA; Saarela, O.; Dendukuri, N., A conditional approach to measure mortality reductions due to cancer screening, Int Stat Rev, 83, 493-510 (2015)
[24] Loeys, T.; Goetghebeur, E., A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance, Biometrics, 59, 100-105 (2003) · Zbl 1210.62182
[25] Loeys, T.; Goetghebeur, E.; Vandebosch, A., Causal proportional hazards models and time-constant exposure in randomized clinical trials, Lifetime Data Anal, 11, 435-449 (2005) · Zbl 1121.62097
[26] Mark, SD; Robins, JM, Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time model, Stat Med, 12, 1605-1628 (1993)
[27] Mark, SD; Robins, JM, A method for the analysis of randomized trials with compliance information: an application to the multiple risk factor intervention trial, Control Clin Trials, 14, 79-97 (1993)
[28] Martinussen, T.; Vansteelandt, S., Instrumental variables estimation with competing risk data, Biostatistics, 21, 158-171 (2020)
[29] Martinussen, T.; Nørbo Sørensen, D.; Vansteelandt, S., Instrumental variables estimation under a structural Cox model, Biostatistics, 20, 65-79 (2019)
[30] McIntosh, MW, Instrumental variables when evaluating screening trials: estimating the benefit of detecting cancer by screening, Stat Med, 18, 2775-2794 (1999)
[31] Miettinen, OS, Toward Scientific Medicine (2014), Berlin: Springer, Berlin
[32] Miettinen, OS, ‘screening’ for breast cancer: Misguided research misinforming public policies, Epidemiologic Methods, 4, 3-10 (2015)
[33] Nie, H.; Cheng, J.; Small, DS, Inference for the effect of treatment on survival probability in randomized trials with noncompliance and administrative censoring, Biometrics, 67, 1397-1405 (2011) · Zbl 1274.62848
[34] NLST Research Team, Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, 395-409 (2011)
[35] Oken, MM; Hocking, WG; Kvale, PA; Andriole, GL; Buys, SS; Church, TR; Crawford, ED; Fouad, MN; Isaacs, C.; Reding, DJ; Weissfeld, JL; Yokochi, LA; O’Brien, B.; Ragard, LR; Rathmell, JM; Riley, TL; Wright, P.; Caparaso, N.; Hu, P.; Izmirlian, G.; Pinsky, PF; Prorok, PC; Kramer, BS; Miller, AB; Gohagan, JK; Berg, CD, Screening by chest radiograph and lung cancer mortality: the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial, JAMA, 306, 1865-1873 (2011)
[36] Putter, H.; Geskus, RB; Fiocco, M., Tutorial in biostatistics: Competing risks and multi-state models, Stat Med, 26, 2389-2430 (2007)
[37] R Core Team (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/
[38] Richardson, A.; Hudgens, MG; Fine, JP; Brookhart, MA, Nonparametric binary instrumental variable analysis of competing risks data, Biostatistics, 18, 48-61 (2017)
[39] Robins, JM, Correcting for non-compliance in randomized trials using structural nested mean models, Commun Stat A-Theor, 23, 2379-2412 (1994) · Zbl 0825.62203
[40] Roemeling, S.; Roobol, MJ; Otto, SJ; Habbema, DF; Gosselaar, C.; Lous, JJ; Cuzick, J.; Schröder, FH, Feasibility study of adjustment for contamination and non-compliance in a prostate cancer screening trial, The Prostate, 67, 1053-1060 (2007)
[41] Saha S, Liu ZA, Saarela O (2018) Estimating case-fatality reduction from randomized screening trials. Epidemiologic Methods 7 · Zbl 1420.92057
[42] Shen, Y.; Zelen, M., Parametric estimation procedures for screening programmes: stable and nonstable disease models for multimodality case finding, Biometrika, 86, 503-515 (1999) · Zbl 0938.62124
[43] Sung, NY; Jun, JK; Kim, YN; Jung, I.; Park, S.; Kim, GR; Nam, CM, Estimating age group-dependent sensitivity and mean sojourn time in colorectal cancer screening, J Med Screen, 26, 19-25 (2019)
[44] Swanson, SA; Robins, JM; Miller, M.; Hernán, MA, Selecting on treatment: a pervasive form of bias in instrumental variable analyses, Am J Epidemiol, 181, 191-197 (2015)
[45] White, IR, Uses and limitations of randomization-based efficacy estimators, Stat Methods Med Res, 14, 327-347 (2005) · Zbl 1122.62376
[46] Young, JG; Stensrud, MJ; Tchetgen Tchetgen, EJ; Hernán, MA, A causal framework for classical statistical estimands in failure-time settings with competing events, Stat Med, 39, 1199-1236 (2020)
[47] Zelen, M., Optimal scheduling of examinations for the early detection of disease, Biometrika, 80, 279-293 (1993) · Zbl 0778.62102
[48] Zheng, C.; Dai, R.; Hari, PN; Zhang, MJ, Instrumental variable with competing risk model, Stat Med, 36, 1240-1255 (2017)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.